Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.
OBJECTIVE
To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes.


METHODS
This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups.


RESULTS
Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power.


CONCLUSIONS
Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.
Maciejewski
Matthew L
ML
Health Services Research and Development, Durham VA Medical Center, Department of Veterans Affairs, Durham, North Carolina, USA. matthew.maciejewski@va.gov
Liu
Chuan-Fen
CF
Fihn
Stephan D
SD
D000368:Aged; D015331:Cohort Studies; D015897:Comorbidity; D048909:Diabetes Complications / Q000191:economics* / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000191:economics* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010045:Outpatients / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D014481:United States; D014728:Veterans / Q000706:statistics & numerical data